System for counting quantity of game tokens

ABSTRACT

A chip recognition system in which a chip is configured to at least partially have a specific color indicative of a value of the chip includes: a recording device that uses a camera and records a state of the chip as an image; an image analysis device that subjects the image so recorded to image analysis and recognizes at least the specific color and a reference color that is present in the image and differs from the specific color; and a recognition device at least including an artificial intelligence device that uses a result of the image analysis by the image analysis device and specifies the specific color of the chip, wherein the artificial intelligence device of the recognition device has been subjected to teaching using, as training data, a plurality of images of the chip and the reference color irradiated with different illumination intensities.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/487,723 filed Aug. 21, 2019, application is a national phaseapplication under 35 U.S.C. § 371 of International Pat. App. No.PCT/JP2018/006247 filed Feb. 21, 2018, which claims priority to JP Pat.App. No. 2017-045443 filed Feb. 21, 2017, the entire contents of eachdisclosure are hereby incorporated by reference.

TECHNICAL FIELD

The present invention relates to a system for recognizing a largequantity of chips used in a game house type-by-type via images, andcounting the quantity of chips type-by-type.

BACKGROUND

Conventionally, in game houses such as casinos, game tokens (referred tohereinafter as “chips”) are used for game betting and payout. In orderto accurately recognize chips stacked on a table, a method is employedof using cameras and recognizing chips as images. WO 2015/107902discloses one example of a system for recognizing the in-game movementof chips by using cameras.

When recognizing chips via images by using cameras, it is requested thatnot only the outlines of the chips be recognized but also thedifferences in colors of the chips be recognized, and further, that thequantity of chips stacked up be grasped for each type of chip. However,chips recognized by cameras, even if the chips have the same color, areimaged or recognized as having different colors due to the difference inlighting environments in which the chips are placed, and are notrecognized as having the same color. Conventionally, it has been thusdifficult for example to count the quantity of chips type-by-type fromimages.

The present invention has been made in view of such a problem, andprovides a chip recognition system having a mechanism enabling therecognition of chip color via images and the counting of the quantity ofchips even under different lighting environments.

SUMMARY OF THE INVENTION

A chip recognition system according to one aspect of the presentinvention is a recognition system for recognizing a chip used on a gametable of a game house, the chip configured to at least partially have aspecific color indicative of a value of the chip, the recognition systemincluding: a recording device that uses a camera and records a state ofthe chip as an image; an image analysis device that subjects the imageso recorded to image analysis and recognizes at least two colors beingthe specific color and a reference color that is present in the imageand differs from the specific color; and a recognition device at leastincluding an artificial intelligence device that uses a result of theimage analysis by the image analysis device and specifies the specificcolor of the chip, wherein the artificial intelligence device of therecognition device has been subjected to teaching using, as trainingdata, a plurality of images of the chip and the reference colorirradiated with different illumination intensities.

Furthermore, the chip at least has the specific color, which isindicative of the value of the chip, at a predetermined position or witha predetermined shape.

Furthermore, the recognition device specifies the specific color of thechip to specify a quantity in which the chip is provided. Furthermore,the recognition device may specify specific colors of a plurality of thechips chip-by-chip to specify a quantity of chips of each of thespecific colors.

Further, the artificial intelligence device of the recognition devicehas been subjected to teaching using, as training data, a plurality ofimages of the reference color and the chip irradiated under differentlighting environments. Further, the artificial intelligence device maydetermine the specific color of the chip by using a relative relationwith the reference color.

The recognition device determines specific colors of a plurality of thechips stacked one on top of another, and may be configured to be capableof determining the specific colors or a quantity of the chips even whensome of the chips are in hidden state due to a dead angle of the camera.

A recognition system for recognizing an article according to one aspectof the present invention, the article at least partially having, on thearticle itself or a wrapping of the article, a specific color enablingthe article or the wrapping to be specified, includes: a recordingdevice that uses a camera and records a state of the article as animage; an image analysis device that subjects the image so recorded toimage analysis and recognizes at least two colors being the specificcolor and a reference color that is present in the image and differsfrom the specific color; and a recognition device at least including anartificial intelligence device that uses a result of the image analysisby the image analysis device and specifies the specific color of thearticle itself or the wrapping, wherein the artificial intelligencedevice of the recognition device has been subjected to teaching using,as training data, a plurality of images of the reference color and thespecific color of the article itself or the wrapping irradiated withdifferent illumination intensities.

Furthermore, the article itself or the wrapping at least partially hasthe specific color, which enables the article or the wrapping to bespecified, at a predetermined position or with a predetermined shape.

Furthermore, the recognition device specifies the specific color of thearticle itself or the wrapping to specify a quantity in which thearticle is provided. The recognition device may specify specific colorsof a plurality of the articles themselves or the wrappings of thearticles article-by-article to specify a quantity of articles of each ofthe specific colors.

Further, the artificial intelligence device of the recognition devicehas been subjected to teaching using, as training data, a plurality ofimages of the reference color and the specific color of the articleitself or the wrapping irradiated under different lighting environments.Further, the artificial intelligence device may determine the specificcolor of the article itself or the wrapping by using a relative relationwith the reference color.

Further, the recognition device determines specific colors of aplurality of the articles stacked one on top of another or the wrappingsof the articles, and is configured to be capable of determining thespecific colors even when some of the articles are in hidden state dueto a dead angle of the camera. Further, the recognition devicedetermines specific colors of a plurality of the articles stacked one ontop of another or the wrappings of the articles, and is configured to becapable of determining a total quantity of the articles or a quantity ofthe articles of each of the specific colors even when some of thearticles are in hidden state due to a dead angle of the camera.

A chip recognition system pertaining to one aspect of the presentinvention is: a chip recognition system for recognizing a chip used on agame table of a game house, the chip configured to at least partiallyhave a specific color indicative of the value of the chip, the chiprecognition system including: a recording device that uses a camera andrecords a state of the chip as an image; an image analysis device thatsubjects the image so recorded to image analysis and recognizes at leasttwo colors being the specific color and a reference color that ispresent in the image and differs from the specific color; and arecognition device at least including an artificial intelligence devicethat uses a result of the image analysis by the image analysis deviceand specifies the specific color of the chip, wherein the artificialintelligence device of the recognition device is configured to extract acenter line from the image of the chip and subject, to image analysis, asurrounding image covering a predetermined range centered on the centerline to recognize, in the surrounding image, at least two colors beingthe specific color and the reference color, which differs from thespecific color, and has been subjected to teaching using, as trainingdata, a plurality of the surrounding images of the chip and thereference color irradiated with different illumination intensities.

A recognition system for recognizing an article, pertaining to oneaspect of the present invention is: a recognition system for recognizingan article, the article at least partially having, on the article itselfor a wrapping of the article, a specific color enabling the article orthe wrapping to be specified, the recognition system including: arecording device that uses a camera and records a state of the articleas an image; an image analysis device that subjects the image sorecorded to image analysis and recognizes at least the specific colorand a reference color that is present in the image and differs from thespecific color; and a recognition device at least including anartificial intelligence device that uses a result of the image analysisby the image analysis device and specifies the specific color of thearticle itself or the wrapping, wherein the artificial intelligencedevice of the recognition device is configured to recognize the specificcolor from the image of the article itself or the wrapping, extract animage portion having the specific color, and subject a surrounding imageof the specific color to image analysis to recognize, in the surroundingimage, at least two colors being the specific color and the referencecolor, and has been subjected to teaching using, as training data, aplurality of the surrounding images of the specific color of the articleitself or the wrapping and the reference color irradiated with differentillumination intensities.

The present invention enables the recognition of chips used on a gametable type-by-type and the counting of the quantity of the chips fromimages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 schematically illustrates a chip recognition system in anembodiment of the present invention.

FIG. 2 is a lateral view of a chip in an embodiment of the presentinvention.

FIG. 3 is a block diagram illustrating a schematic configuration of achip recognition system in an embodiment of the present invention.

FIG. 4 schematically illustrates an article recognition system inanother embodiment of the present invention.

FIG. 5 is a diagram for explaining another example for determining chipcolor.

FIG. 6 includes a lateral view of a chip and an enlarged diagramthereof.

DETAILED DESCRIPTION OF EMBODIMENTS

The following describes embodiments of the present invention in detail,with reference to the attached drawings. Note that the same symbols areprovided to constituent elements having equivalent functions in thedrawings, and detailed description regarding constituent elementsprovided with the same symbol is not repeated.

FIG. 1 schematically illustrates a chip recognition system 10 in anembodiment of the present invention. As illustrated in FIG. 1, in thepresent embodiment, a camera 212 for imaging a state of chips W stackedone on top of another on a game table 4 is provided at the outside ofthe game table 4.

In the chip recognition system 10 according to the present embodiment, achip W is configured to at least partially have a specific color 121indicative of the value thereof, as illustrated in FIG. 2. Further, thechip recognition system 10 according to the present embodiment includes:a recording device 11 that uses the camera 212 and records a state ofthe chip W as an image; an image analysis device 14 that subjects theimage so recorded to image analysis and recognizes at least the specificcolor 121 and a reference color R that is present in the image anddiffers from the specific color 121; and a recognition device 12 atleast including an artificial intelligence device 12 a that uses aresult of the image analysis by the image analysis device 14 andspecifies the specific color 121 of the chip W, wherein the artificialintelligence device 12 a of the recognition device 12 has been subjectedto teaching using, as training data, a plurality of images of the chip Wand the reference color R irradiated with different illuminationintensities.

Note that the chip recognition system 10 according to the presentembodiment is connected in communicable state with respect to the camera212.

FIG. 3 is a block diagram illustrating a schematic configuration of thechip recognition system 10 according to the present embodiment.

As illustrated in FIG. 3, the chip recognition system 10 includes: therecording device 11; the recognition device 12; a learning machine 13;and the image analysis device 14. Note that at least part of the chiprecognition system 10 is realized by using a computer.

The recording device 11 includes a stationary data storage such as ahard disk, for example. The recording device 11 records a state of achip W stacked on the game table 4 as an image captured by the camera212. Note that the image may be a moving image or may be successivestill images.

The recording device 11 may append an index or time with respect to theimage acquired from the camera 212, so that imaging history can be lateranalyzed by the later-described recognition device.

The image analysis device 14 subjects the image recorded by therecording device 11 to image analysis and recognizes at least two colorsbeing the specific color 121, which is at least partially provided tothe chip W, and the reference color R that is present in the image anddiffers from the specific color 121. Note that the specific color 121 isprovided at least partially to the chip W at a predetermined position orwith a predetermined shape. For example, the specific color 121 may beprovided on a lateral surface of the chip W in the circumferentialdirection, or may be provided as a predetermined mark on a surface ofthe chip W. Meanwhile, the reference color R may for example be a colorof a specific area of the game table 4 or a color provided to a positionof the chip W differing from the position of the specific color 121.

The recognition device 12 includes the artificial intelligence device 12a, which uses the result of the image analysis by the image analysisdevice 14 and specifies the specific color 121 by using deep learningtechnology, for example. The recognition device 12 determines thequantity and types of the chips W placed on the game table 4. Therecognition device 12 may further determine the positions of the chips Won the game table 4.

As illustrated in FIG. 3, the recognition device 12 outputs a result ofthe determination to an output device 15. The output device 15 mayoutput the determination result received from the recognition device 12as text information to a monitor above the game table 4, etc.

In the present embodiment, the learning machine 13 acquires, via theimage analysis device 14, a plurality of images, recorded by therecording device 11, of the chip W and the reference color R irradiatedwith different illumination intensities. Further, the learning machine13 undergoes learning by being subjected to teaching by a person usingthe acquired images and the correct colors of the specific color 121 ofthe chip W and the reference color R in the respective images astraining data, and creates a learning model 13 a (recognition program).Note that the relative relation between the specific color 121 and thereference color R can be acquired from images of the chip W and thereference color R irradiated with illumination intensities of the samecondition, due to the specific color 121 and the reference color R beingirradiated with the same illumination intensity. This relative relation,for example, may be utilized in the recognition of the specific color121. Images each of which was acquired by irradiating from differentirradiation angles or images each of which was created by arbitrarilychanging the distribution of RGB values of the acquired images may beused as training data.

By a teaching operation being repeated in which a person inputs theabove-described training data to the learning machine 13 and causes thelearning machine 13 to undergo learning, the accuracy of specificationof the specific color 121 of the chip W by the learning model 13 apossessed by the learning machine 13 can be improved. The learningmachine 13 is capable of creating a learning model 13 a with which it ispossible to determine specific colors 121 of a plurality of chips Wplaced on the game table 4 even when some of the chips W on the gametable 4 are in hidden state due to a dead angle of the camera 212, byrepeating learning of such images in advance.

The learning model 13 a so created can be input to the artificialintelligence device 12 a via an external medium such as a USB memory, aHDD, etc., or a communication network, etc.

Further, as illustrated in FIG. 3, images of the chip W and thereference color R, and the result of the determination by therecognition device 12 may be input to the learning machine 13 astraining data.

Note that various modifications can be made based on the above-describedembodiment. The following describes one example of a modification, withreference to the drawings. Note that in the following description andthe drawings used in the following description, the same symbols as usedfor the corresponding portions in the above-described embodiment areused for portions that could be configured similarly to theabove-described embodiment, and redundant description is also omitted.

FIG. 4 schematically illustrates an article recognition system 20 inanother embodiment of the present invention. As illustrated in FIG. 4,in the present embodiment, a camera 212 for imaging a state of articlesB placed on an article display shelf 5 is provided at the outside of thearticle display shelf 5.

Further, in the article recognition system 20 according to the presentembodiment, an article B is configured to at least partially have aspecific color 121 on the article itself or a wrapping of the article.The specific color 121 enables the article or the wrapping to bespecified. Further, the article recognition system 20 according to thepresent embodiment includes: a recording device 11 that uses the camera212 and records a state of the article B as an image; an image analysisdevice 14 that subjects the image so recorded to image analysis andrecognizes at least two colors being the specific color 121 and areference color R that is present in the image and differs from thespecific color 121; and a recognition device 12 at least including anartificial intelligence device 12 a that uses a result of the imageanalysis by the image analysis device 14 and specifies the specificcolor 121 of the article B, wherein the artificial intelligence device12 a of the recognition device 12 has been subjected to teaching using,as training data, a plurality of images of the reference color R and thespecific color 121 of the article B itself or the wrapping irradiatedwith different illumination intensities.

Note that the article recognition system 20 according to the presentembodiment is connected in communicable state with respect to the camera212.

The article recognition system 20 includes: the recording device 11; therecognition device 12; a learning machine 13; and the image analysisdevice 14. Note that at least part of the article recognition system 20is realized by using a computer.

The recording device 11 includes a stationary data storage such as ahard disk, for example. The recording device 11 records a state of anarticle B placed on the article display shelf 5 as an image captured bythe camera 212. Note that the image may be a moving image or may besuccessive still images.

The recording device 11 may append an index or time with respect to theimages acquired from the camera 212, so that imaging history can belater analyzed by the later-described recognition device.

The image analysis device 14 subjects the image recorded by therecording device 11 to image analysis and recognizes at least two colorsbeing the specific color 121, which is at least partially provided tothe article B, and the reference color R, which is present in the imageand differs from the specific color 121. The specific color 121, whichis provided to the article B itself or a wrapping thereof, is at leastpartially provided to the article B itself or the wrapping thereof at apredetermined position or with a predetermined shape, and may beprovided at any position of the article B itself or the wrapping thereofand may have various shapes. Meanwhile, the reference color R may forexample be a color of a part of a frame of the article display shelf 5or a color of a wall in the background.

The recognition device 12 includes the artificial intelligence device 12a, which uses the result of the image analysis by the image analysisdevice 14 and specifies the specific color 121 by using deep learningtechnology, for example. The recognition device 12 determines thequantity and types of articles B placed on the article display shelf 5.The recognition device 12 may further determine the positions of thearticles B placed on the article display shelf 5.

In the present embodiment, the learning machine 13 acquires, via theimage analysis device 14, a plurality of images, recorded by therecording device 11, of the article B itself or the wrapping thereof andthe reference color R irradiated with different illuminationintensities. Further, the learning machine 13 undergoes learning bybeing subjected to teaching by a person using the acquired images andthe correct colors of the specific color 121 provided to the article Bitself or the wrapping thereof and the reference color R in therespective images as training data, and creates a learning model 13 a(recognition program). Note that the relative relation between thespecific color 121 and the reference color R can be acquired from imagesof article B and the reference color R irradiated with illuminationintensities of the same condition, due to the specific color 121 and thereference color R being irradiated with the same illumination intensity.This relative relation, for example, may be utilized in the recognitionof the specific color 121. Images each of which was acquired byirradiating from different irradiation angles or images each of whichwas created by arbitrarily changing the distribution of RGB values ofthe acquired images may be used as training data.

By a teaching operation being repeated in which a person inputs theabove-described training data to the learning machine 13 and causes thelearning machine 13 to undergo learning, the accuracy of specificationof the specific color 121 provided to the article B itself or thewrapping thereof by the learning model 13 a possessed by the learningmachine 13 can be improved. The learning machine 13 is capable ofcreating a learning model 13 a with which it is possible to determinespecific colors 121 of a plurality of articles B placed on the articledisplay shelf 5 even when some of the articles B on the article displayshelf 5 are in hidden state due to a dead angle of the camera 212, byrepeating learning of such images in advance.

The learning model 13 a so created can be input to the artificialintelligence device 12 a via an external medium such as a USB memory, aHDD, etc., or a communication network, etc.

Further, as illustrated in FIG. 3, images of the specific color 121 ofthe article B or the wrapping thereof and the reference color R, and theresult of the determination by the recognition device 12 may be input tothe learning machine 13 as training data.

FIG. 5 schematically illustrates another example for determining chipcolor. In the present embodiment, the artificial intelligence device 12a of the recognition device 12 extracts a center line C of a chip W froman image of the chip W by using artificial intelligence.

Specifically, as illustrated in FIG. 3, the learning machine 13acquires, via the image analysis device 14, a plurality of images,recorded by the recording device 11, of the center line C of the chip Wirradiated with different illumination intensities. Further, thelearning machine 13 undergoes learning by being subjected to teaching bya person using the acquired images and the correct positions of thecenter line C of the chip Win the respective images as training data,and creates a learning model 13 a (recognition program).

By a teaching operation being repeated in which a person inputs theabove-described training data to the learning machine 13 and causes thelearning machine 13 to undergo learning, the accuracy of specificationof the center line C of the chip W by the learning model 13 a possessedby the learning machine 13 can be improved. The learning machine 13 iscapable of creating a learning model 13 a with which it is possible todetermine center lines C of a plurality of chips W placed on the gametable 4 even when some of the chips Won the game table 4 are in hiddenstate due to a dead angle of the camera 212, by repeating learning ofsuch images in advance.

The learning model 13 a so created is input to the artificialintelligence device 12 a via an external medium such as a USB memory, aHDD, etc., or a communication network, etc., whereby the artificialintelligence device 12 a becomes capable of extracting the center line Cof the chip W from the image of the chip W by using artificialintelligence.

Note that image analysis of the center line C from the image may beperformed by analyzing the image in its original state or aftersubjecting the image to image processing such as color emphasis, noiseremoval, etc., in order to facilitate the recognition of the center lineC.

Further, the recognition device 12, without using artificialintelligence, may extract the center line C of the chip W through amethod of using the result of the imaging by the camera 212, therecording as the image, and further the image analysis, and measuringimage features such as shapes, brightness, chroma, and hue.

As illustrated in FIG. 6, the artificial intelligence device 12 a isfurther configured to subject, to image analysis, a surrounding imagecovering a predetermined range around the extracted center line C (forexample, a range centered on the center line C and corresponding toeight pixels perpendicular to the center line C) to recognize, in thesurrounding image, at least two colors being the specific color 121 andthe reference color R, which differs from the specific color 121. Notethat image analysis of the surrounding image of the predetermined rangearound the extracted center line C may be performed by analyzing theimage in its original state or after subjecting the image to imageprocessing such as color emphasis, noise removal, etc., in order tofacilitate the recognition of the specific color 121.

The artificial intelligence device 12 a has been subjected to teachingusing, as training data, a plurality of images of the chip W and thereference color R irradiated with different illumination intensities.Note that the relative relation between the specific color 121 and thereference color R can be acquired from surrounding images of the centerline C of the chip W irradiated with illumination intensities of thesame condition, due to the specific color 121 and the reference color Rbeing irradiated with the same illumination intensity. This relativerelation, for example, may be utilized in the recognition of thespecific color 121.

Further, the recognition device 12, without using artificialintelligence, may recognize the specific color 121 through a method ofusing the result of the imaging by the camera 212, the recording as theimage, and further the image analysis, and measuring image features suchas shapes, brightness, chroma, and hue.

In summary, the artificial intelligence device 12 a of the recognitiondevice 12 is configured to extract a center line C from an image of achip W and subject, to image analysis, a surrounding image covering apredetermined range centered on the center line C to recognize, in thesurrounding image, at least two colors being a specific color 121 and areference color R, which differs from the specific color 121, and hasbeen subjected to teaching using, as training data, a plurality of thesurrounding images of the chip W and the reference color R irradiatedwith different illumination intensities.

In another embodiment for determining an article, the artificialintelligence device 12 a of the article recognition device 12 extracts aspecific color 121 of an article B itself or a wrapping of the article Bfrom an image of the article B itself or the wrapping thereof by usingartificial intelligence.

Specifically, as illustrated in FIG. 3, the learning machine 13acquires, via the image analysis device 14, a plurality of images,recorded by the recording device 11, of the article B itself or thewrapping thereof and the reference color R irradiated with differentillumination intensities. Further, the learning machine 13 undergoeslearning by being subjected to teaching by a person using the acquiredimages and the correct positions of the specific color 121 provided tothe article B itself or the wrapping thereof in the respective images astraining data, and creates a learning model 13 a (recognition program).

By a teaching operation being repeated in which a person inputs theabove-described training data to the learning machine 13 and causes thelearning machine 13 to undergo learning, the accuracy of specificationof the specific color 121 provided to the article B itself or thewrapping thereof by the learning model 13 a possessed by the learningmachine 13 can be improved. The learning machine 13 is capable ofcreating a learning model 13 a with which it is possible to determinespecific colors 121 of a plurality of articles B placed on the articledisplay shelf 5 even when some of the articles B on the article displayshelf 5 are in hidden state due to a dead angle of the camera 212, byrepeating learning of such images in advance.

The learning model 13 a so created is input to the artificialintelligence device 12 a via an external medium such as a USB memory, aHDD, etc., or a communication network, etc., whereby the artificialintelligence device 12 a becomes capable of extracting a portion havingthe specific color 121 provided to the article B itself or the wrappingthereof from the image of the article B by using artificialintelligence.

Note that image analysis of the portion having the specific color 121from the image may be performed by analyzing the image in its originalstate or after subjecting the image to image processing such as coloremphasis, noise removal, etc., in order to facilitate the recognition ofthe portion having the specific color 121.

Further, the recognition device 12, without using artificialintelligence, may recognize the portion having the specific color 121 ofthe article B itself or the wrapping thereof through a method ofmeasuring image features such as shapes, brightness, chroma, and hue.

The artificial intelligence device 12 a is further configured tosubject, to image analysis, a surrounding image covering a predeterminedrange around the portion having the specific color 121 (for example, arange corresponding to eight pixels around the portion having thespecific color 121) to recognize, in the surrounding image, at least twocolors being the specific color 121 and the reference color R, whichdiffers from the specific color 121. Note that image analysis of thesurrounding image of the predetermined range around the portion havingthe specific color 121 may be performed by analyzing the image in itsoriginal state or after subjecting the image to image processing such ascolor emphasis, noise removal, etc., in order to facilitate therecognition of the portion having the specific color 121.

The artificial intelligence device 12 a has been subjected to teachingusing, as training data, a plurality of images of the portion having thespecific color 121 of the article B itself or the wrapping thereof andthe reference color R irradiated with different illuminationintensities. Note that the relative relation between the specific color121 and the reference color R can be acquired from surrounding images ofthe portion having the specific color 121 of the article B itself or thewrapping thereof irradiated under illumination intensities of the samecondition, due to the specific color 121 and the reference color R beingirradiated with the same illumination intensity. This relative relation,for example, may be utilized in the recognition of the specific color121. Images each of which was acquired by irradiating from differentirradiation angles or images each of which was created by arbitrarilychanging the distribution of RGB values of the acquired images may beused as training data.

Further, the recognition device 12, without using artificialintelligence, may recognize the specific color 121 through a method ofusing the result of the imaging by the camera 212, the recording as theimage, and further the image analysis, and measuring image features suchas shapes, brightness, chroma, and hue.

In summary, the artificial intelligence device 12 a of the recognitiondevice 12 is configured to recognize a specific color 121 from an imageof an article B itself or a wrapping thereof, extract an image portionhaving the specific color 121, and subject a surrounding image of thespecific color 121 to image analysis to recognize, in the surroundingimage, at least two colors being the specific color 121 and thereference color R, and has been subjected to teaching using, as trainingdata, a plurality of the surrounding images of the specific color 121 ofthe article B itself or the wrapping thereof and the reference color Rirradiated with different illumination intensities.

The above-described embodiments are disclosed for the purpose ofallowing those having ordinary knowledge in the technical field to whichthe present invention belongs to implement the present invention. Thoseskilled in the art could naturally make various modifications of theabove-described embodiments, and the technical concept of the presentinvention is also applicable to other embodiments. Accordingly, thepresent invention is not limited to the embodiments disclosed herein,and shall be construed as having the broadest scope in accordance withthe technical concept defined by the patent claims.

DESCRIPTION OF THE REFERENCE NUMERALS

-   4 Game Table-   5 Output Device-   10 Chip Recognition System-   11 Recording Device-   12 Recognition Device-   12 a Artificial Intelligence Device-   13 Learning Machine-   13 a Learning Model-   14 Image Analysis Device-   15 Output Device-   20 Article Recognition System-   121 Specific Color-   212 Camera-   W Chip-   B Article-   R Reference Color-   C Center Line

What is claimed is:
 1. A chip recognition system for recognizing chipsto be used at a game table in an amusement facility, the chiprecognition system comprising: a recording device configured to record astate of a chip as an image captured by a camera, the chip is configuredto include at least a specific color indicating a value of the chip; anda recognition device including at least an artificial intelligencedevice configured to identify, based on image analysis of the recordedimage, the specific color of the chip that is represented in the imagein a different color, based on a lighting environment, than the specificcolor of the chip identify a specific color of the chip.
 2. The chiprecognition system according to claim 1, wherein the chip has at leastthe specific color indicating the value of the chip at a predeterminedlocation or in a predetermined shape.
 3. The chip recognition systemaccording to claim 1, wherein the recognition device is furtherconfigured to identify a number of chips based on identification of thespecific color of the chip.
 4. The chip recognition system according toclaim 1, wherein the recognition device further is configured toidentify a number of chips for each specific color of a plurality ofspecific colors by identifying, for each chip of a plurality of thechips, the specific color.
 5. The chip recognition system according toclaim 1, wherein the artificial intelligence device of the recognitiondevice is an artificial intelligence device taught with a plurality ofimages of a predetermined reference color different from the specificcolor and chips irradiated in different lighting environments as teacherdata.
 6. The chip recognition system according to claim 5, wherein thereference color is a color that chips of different types have in common.7. The chip recognition system according to claim 1, wherein theartificial intelligence device of the recognition device is anartificial intelligence device configured to determine the specificcolor of the chip using a relative relationship with a predeterminedreference color different from the specific color.
 8. The chiprecognition system according to claim 1, wherein the recognition deviceis configured to: determine the specific color of a plurality of chipsstacked on top of each other, and determine the specific color or numberof chips when the chips are partially hidden due to a blind spot of thecamera.
 9. A recognition system for recognizing an article, comprising:a recording device configured to record a state of an article as animage using a camera, the article has a structure in which the articleitself or the packaging has at least a specific color that can identifythe article or the packaging; and a recognition device including atleast an artificial intelligence device configured to identify aspecific color of the article itself or packaging by image analysis ofthe recorded image, wherein the artificial intelligence device of therecognition device is an artificial intelligence device taught with aplurality of images including the specific color of the article itselfor packaging that is represented in the image in a different color thanthe specific color of the chip based on a lighting environment asteacher data.
 10. The recognition system according to claim 9, whereinthe article itself or the packaging has at least the specific color in apredetermined position or in a predetermined shape to enableidentification of the article or the packaging.
 11. The recognitionsystem according to claim 9, wherein the recognition device isconfigured to identify a number of articles based on identification ofthe specific color of the article itself or packaging.
 12. Therecognition system according to claim 11, wherein the recognition deviceis configured to identify a number of pieces of each of the articlesbased on identification of the specific color of a plurality of articlesthemselves or packaging for each article.
 13. The recognition systemaccording to claim 9, wherein the artificial intelligence device of therecognition device is an artificial intelligence device taught with aplurality of images of a predetermined reference color different fromthe specific color and the specific color of the article itself orpackaging illuminated in different lighting environments as teacherdata.
 14. The recognition system according to claim 9, wherein theartificial intelligence device of the recognition device is anartificial intelligence device configured to determine the specificcolor of the article itself or the packaging using a relativerelationship with a predetermined reference color that is different fromthe specific color.
 15. The recognition system according to claim 9,wherein the recognition device configured to: determine the specificcolor of a plurality of stacked articles themselves or packaging, anddetermine the specific color if a part of the article is hidden due to ablind spot of the camera.
 16. The recognition system according to claim9, wherein the recognition device is configured to: determine thespecific color of a plurality of stacked articles themselves or theirpackaging, and determine the total number of articles or the number ofarticles of each specific color, when the articles are partially hiddendue to a blind spot of the camera.